Overview

Dataset statistics

Number of variables15
Number of observations1000000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory107.8 MiB
Average record size in memory113.0 B

Variable types

NUM14
CAT1

Reproduction

Analysis started2020-07-31 08:04:18.952002
Analysis finished2020-07-31 08:10:38.903841
Duration6 minutes and 19.95 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Variables

symboling
Categorical

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size976.9 KiB
0
289560
1
273678
2
180133
-1
124271
3
86914
Other values (2)
 
45444
ValueCountFrequency (%) 
028956029.0%
 
127367827.4%
 
218013318.0%
 
-112427112.4%
 
3869148.7%
 
-2312413.1%
 
-3142031.4%
 
2020-07-31T10:10:42.279031image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.169715
Min length1

normalized-losses
Real number (ℝ≥0)

Distinct count995457
Unique (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.70330671815898
Minimum40.534787
Maximum282.388034
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:42.852038image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum40.534787
5-th percentile74.87666125
Q195.43048225
median113.370858
Q3142.4856448
95-th percentile190.62502
Maximum282.388034
Range241.853247
Interquartile range (IQR)47.0551625

Descriptive statistics

Standard deviation35.13634235
Coefficient of variation (CV)0.2910967669
Kurtosis-0.002705286066
Mean120.7033067
Median Absolute Deviation (MAD)22.6180315
Skewness0.7632331042
Sum120703306.7
Variance1234.562553
2020-07-31T10:10:42.958200image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
102.1816333< 0.1%
 
114.5176393< 0.1%
 
77.9186213< 0.1%
 
143.04253< 0.1%
 
77.8517033< 0.1%
 
93.9768433< 0.1%
 
97.1730233< 0.1%
 
96.1525313< 0.1%
 
101.2449963< 0.1%
 
115.4824653< 0.1%
 
Other values (995447)999970> 99.9%
 
ValueCountFrequency (%) 
40.5347871< 0.1%
 
41.689831< 0.1%
 
42.1587351< 0.1%
 
43.48631< 0.1%
 
45.3135061< 0.1%
 
ValueCountFrequency (%) 
282.3880341< 0.1%
 
273.3944941< 0.1%
 
271.2675751< 0.1%
 
269.7395211< 0.1%
 
269.4456371< 0.1%
 

wheel-base
Real number (ℝ≥0)

Distinct count958623
Unique (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.06851651822105
Minimum82.937917
Maximum120.491583
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:43.504687image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum82.937917
5-th percentile91.4276658
Q194.8503675
median96.756566
Q3100.353569
95-th percentile108.7662701
Maximum120.491583
Range37.553666
Interquartile range (IQR)5.5032015

Descriptive statistics

Standard deviation5.07963421
Coefficient of variation (CV)0.05179678852
Kurtosis0.4516140198
Mean98.06851652
Median Absolute Deviation (MAD)2.4060295
Skewness0.9574328535
Sum98068516.52
Variance25.80268371
2020-07-31T10:10:43.614722image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
92.8625935< 0.1%
 
95.2273885< 0.1%
 
94.9390584< 0.1%
 
96.8530684< 0.1%
 
96.6808914< 0.1%
 
96.7303164< 0.1%
 
95.4061884< 0.1%
 
96.6035484< 0.1%
 
97.0245394< 0.1%
 
96.8168324< 0.1%
 
Other values (958613)999958> 99.9%
 
ValueCountFrequency (%) 
82.9379171< 0.1%
 
83.6020431< 0.1%
 
84.0372431< 0.1%
 
84.2898961< 0.1%
 
84.4545391< 0.1%
 
ValueCountFrequency (%) 
120.4915831< 0.1%
 
120.3796851< 0.1%
 
120.0185091< 0.1%
 
119.6941571< 0.1%
 
119.0986441< 0.1%
 

length
Real number (ℝ≥0)

Distinct count984262
Unique (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean172.2793806400221
Minimum135.551814
Maximum207.326158
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:44.182307image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum135.551814
5-th percentile152.8070862
Q1166.1661565
median172.262017
Q3178.966987
95-th percentile191.2034415
Maximum207.326158
Range71.774344
Interquartile range (IQR)12.8008305

Descriptive statistics

Standard deviation11.11342729
Coefficient of variation (CV)0.06450816834
Kurtosis-0.344536897
Mean172.2793806
Median Absolute Deviation (MAD)6.3250765
Skewness-0.0366286563
Sum172279380.6
Variance123.5082661
2020-07-31T10:10:44.310302image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
171.9150574< 0.1%
 
172.8919854< 0.1%
 
171.8800644< 0.1%
 
153.0430854< 0.1%
 
166.9381043< 0.1%
 
172.993273< 0.1%
 
170.4542383< 0.1%
 
172.7764583< 0.1%
 
173.2438363< 0.1%
 
173.7570123< 0.1%
 
Other values (984252)999966> 99.9%
 
ValueCountFrequency (%) 
135.5518141< 0.1%
 
135.8589351< 0.1%
 
136.1577761< 0.1%
 
136.3142461< 0.1%
 
136.3316461< 0.1%
 
ValueCountFrequency (%) 
207.3261581< 0.1%
 
206.5495371< 0.1%
 
205.9297321< 0.1%
 
204.7953051< 0.1%
 
203.4023171< 0.1%
 

width
Real number (ℝ≥0)

Distinct count890886
Unique (%)89.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.55292421936302
Minimum61.012905
Maximum74.819282
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:44.827676image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum61.012905
5-th percentile63.1373288
Q164.16206375
median65.350526
Q366.387642
95-th percentile69.51359005
Maximum74.819282
Range13.806377
Interquartile range (IQR)2.22557825

Descriptive statistics

Standard deviation1.888488794
Coefficient of variation (CV)0.02880861253
Kurtosis0.57168452
Mean65.55292422
Median Absolute Deviation (MAD)1.132618
Skewness0.9454372789
Sum65552924.22
Variance3.566389926
2020-07-31T10:10:44.948416image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
65.3865338< 0.1%
 
65.3833347< 0.1%
 
65.338987< 0.1%
 
65.4475256< 0.1%
 
65.4223356< 0.1%
 
65.3602466< 0.1%
 
65.3468516< 0.1%
 
65.3732196< 0.1%
 
65.3338246< 0.1%
 
65.4111866< 0.1%
 
Other values (890876)999936> 99.9%
 
ValueCountFrequency (%) 
61.0129051< 0.1%
 
61.0244551< 0.1%
 
61.0278991< 0.1%
 
61.0755251< 0.1%
 
61.0777711< 0.1%
 
ValueCountFrequency (%) 
74.8192821< 0.1%
 
74.7134261< 0.1%
 
74.197411< 0.1%
 
74.1297751< 0.1%
 
74.1027011< 0.1%
 

height
Real number (ℝ≥0)

Distinct count935857
Unique (%)93.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.873614720735
Minimum47.830953
Maximum62.224857
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:45.482322image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum47.830953
5-th percentile50.2856807
Q152.3209935
median53.946569
Q355.3061815
95-th percentile57.8678307
Maximum62.224857
Range14.393904
Interquartile range (IQR)2.985188

Descriptive statistics

Standard deviation2.255409585
Coefficient of variation (CV)0.04186482746
Kurtosis-0.4408612577
Mean53.87361472
Median Absolute Deviation (MAD)1.5011835
Skewness0.1882725852
Sum53873614.72
Variance5.086872396
2020-07-31T10:10:45.604748image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
52.2747485< 0.1%
 
55.1432325< 0.1%
 
53.7142444< 0.1%
 
54.7054734< 0.1%
 
54.849294< 0.1%
 
55.0669254< 0.1%
 
52.3048524< 0.1%
 
52.6053884< 0.1%
 
55.4908854< 0.1%
 
52.2901934< 0.1%
 
Other values (935847)999958> 99.9%
 
ValueCountFrequency (%) 
47.8309531< 0.1%
 
47.8904991< 0.1%
 
47.9431581< 0.1%
 
48.0247271< 0.1%
 
48.1124761< 0.1%
 
ValueCountFrequency (%) 
62.2248571< 0.1%
 
62.1572431< 0.1%
 
62.1509851< 0.1%
 
62.1499631< 0.1%
 
62.0742711< 0.1%
 

curb-weight
Real number (ℝ≥0)

Distinct count999672
Unique (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2450.2383411871415
Minimum1488.079382
Maximum4365.473381
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:46.325551image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1488.079382
5-th percentile1845.645156
Q12094.865045
median2345.431832
Q32749.398274
95-th percentile3385.17949
Maximum4365.473381
Range2877.393999
Interquartile range (IQR)654.5332285

Descriptive statistics

Standard deviation471.8533317
Coefficient of variation (CV)0.1925744625
Kurtosis-0.1542935494
Mean2450.238341
Median Absolute Deviation (MAD)308.7316065
Skewness0.7621834777
Sum2450238341
Variance222645.5666
2020-07-31T10:10:46.440295image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2316.5545232< 0.1%
 
2309.5468452< 0.1%
 
2035.8978232< 0.1%
 
1878.2443262< 0.1%
 
2750.3515162< 0.1%
 
2836.8726942< 0.1%
 
2020.7423042< 0.1%
 
2125.3509152< 0.1%
 
2183.0550352< 0.1%
 
2219.2633142< 0.1%
 
Other values (999662)999980> 99.9%
 
ValueCountFrequency (%) 
1488.0793821< 0.1%
 
1503.7012791< 0.1%
 
1508.5253151< 0.1%
 
1511.7971071< 0.1%
 
1513.0309861< 0.1%
 
ValueCountFrequency (%) 
4365.4733811< 0.1%
 
4300.6589281< 0.1%
 
4275.3299931< 0.1%
 
4268.894441< 0.1%
 
4266.8517291< 0.1%
 

engine-size
Real number (ℝ≥0)

Distinct count986280
Unique (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean117.98050930962303
Minimum47.038906
Maximum270.101636
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:47.002905image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum47.038906
5-th percentile85.8324626
Q197.334543
median109.1238825
Q3127.555666
95-th percentile183.2597272
Maximum270.101636
Range223.06273
Interquartile range (IQR)30.221123

Descriptive statistics

Standard deviation29.56153817
Coefficient of variation (CV)0.2505628967
Kurtosis1.339278696
Mean117.9805093
Median Absolute Deviation (MAD)13.5551745
Skewness1.342225846
Sum117980509.3
Variance873.884539
2020-07-31T10:10:47.106240image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
108.3704744< 0.1%
 
108.3490554< 0.1%
 
109.0437634< 0.1%
 
109.1934694< 0.1%
 
99.508274< 0.1%
 
109.4776264< 0.1%
 
97.3394923< 0.1%
 
107.8137343< 0.1%
 
109.0099793< 0.1%
 
108.4450353< 0.1%
 
Other values (986270)999964> 99.9%
 
ValueCountFrequency (%) 
47.0389061< 0.1%
 
52.637091< 0.1%
 
53.7389051< 0.1%
 
55.5382811< 0.1%
 
56.8754761< 0.1%
 
ValueCountFrequency (%) 
270.1016361< 0.1%
 
267.268221< 0.1%
 
267.0002311< 0.1%
 
266.233281< 0.1%
 
266.2017961< 0.1%
 

bore
Real number (ℝ≥0)

Distinct count612985
Unique (%)61.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3017691945530006
Minimum2.596391
Maximum4.051284
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:47.467967image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2.596391
5-th percentile2.908112
Q13.10495
median3.250946
Q33.52834875
95-th percentile3.73413105
Maximum4.051284
Range1.454893
Interquartile range (IQR)0.42339875

Descriptive statistics

Standard deviation0.2628983532
Coefficient of variation (CV)0.07962347992
Kurtosis-0.9976579628
Mean3.301769195
Median Absolute Deviation (MAD)0.201378
Skewness0.2064503862
Sum3301769.195
Variance0.06911554409
2020-07-31T10:10:47.584326image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.15192110< 0.1%
 
3.1488510< 0.1%
 
3.2085489< 0.1%
 
3.2546829< 0.1%
 
3.1480439< 0.1%
 
3.1468529< 0.1%
 
3.128339< 0.1%
 
3.1846929< 0.1%
 
3.2356699< 0.1%
 
3.2137089< 0.1%
 
Other values (612975)999908> 99.9%
 
ValueCountFrequency (%) 
2.5963911< 0.1%
 
2.5982111< 0.1%
 
2.6048981< 0.1%
 
2.6104131< 0.1%
 
2.6104811< 0.1%
 
ValueCountFrequency (%) 
4.0512841< 0.1%
 
4.0511911< 0.1%
 
4.0335331< 0.1%
 
4.0335161< 0.1%
 
4.0330151< 0.1%
 

stroke
Real number (ℝ≥0)

Distinct count598812
Unique (%)59.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.239506486957
Minimum1.710764
Maximum4.184797
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:47.948007image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum1.710764
5-th percentile2.6147529
Q13.11806875
median3.278242
Q33.421419
95-th percentile3.67668615
Maximum4.184797
Range2.474033
Interquartile range (IQR)0.30335025

Descriptive statistics

Standard deviation0.30054921
Coefficient of variation (CV)0.09277623342
Kurtosis1.144027532
Mean3.239506487
Median Absolute Deviation (MAD)0.149862
Skewness-0.8735938003
Sum3239506.487
Variance0.09032982763
2020-07-31T10:10:48.061969image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
3.41876313< 0.1%
 
3.40479413< 0.1%
 
3.41881112< 0.1%
 
3.40146112< 0.1%
 
3.40410812< 0.1%
 
3.40431111< 0.1%
 
3.42773711< 0.1%
 
3.41374311< 0.1%
 
3.43067811< 0.1%
 
3.40993111< 0.1%
 
Other values (598802)999883> 99.9%
 
ValueCountFrequency (%) 
1.7107641< 0.1%
 
1.7154951< 0.1%
 
1.717371< 0.1%
 
1.7295031< 0.1%
 
1.7637561< 0.1%
 
ValueCountFrequency (%) 
4.1847971< 0.1%
 
4.1715171< 0.1%
 
4.1686281< 0.1%
 
4.1665811< 0.1%
 
4.1644881< 0.1%
 

compression-ratio
Real number (ℝ)

Distinct count649974
Unique (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.116499157775996
Minimum-12.104269
Maximum43.255678
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:48.448099image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-12.104269
5-th percentile7.5036708
Q18.6460055
median9
Q39.35974625
95-th percentile19.70514305
Maximum43.255678
Range55.359947
Interquartile range (IQR)0.71374075

Descriptive statistics

Standard deviation3.908532595
Coefficient of variation (CV)0.3863522879
Kurtosis7.636462709
Mean10.11649916
Median Absolute Deviation (MAD)0.3571715
Skewness2.591073397
Sum10116499.16
Variance15.27662705
2020-07-31T10:10:48.565738image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
922328822.3%
 
9.3679768< 0.1%
 
8.6790948< 0.1%
 
9.3448028< 0.1%
 
8.7077047< 0.1%
 
9.3515527< 0.1%
 
9.2930217< 0.1%
 
9.3486547< 0.1%
 
8.7042047< 0.1%
 
9.3131527< 0.1%
 
Other values (649964)77664677.7%
 
ValueCountFrequency (%) 
-12.1042691< 0.1%
 
-10.4281911< 0.1%
 
-10.2950511< 0.1%
 
-10.1595111< 0.1%
 
-10.0187691< 0.1%
 
ValueCountFrequency (%) 
43.2556781< 0.1%
 
42.1145371< 0.1%
 
41.558521< 0.1%
 
41.1241011< 0.1%
 
40.6328991< 0.1%
 

horsepower
Real number (ℝ≥0)

Distinct count993654
Unique (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.22985335323202
Minimum36.841507
Maximum232.513224
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:49.134831image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum36.841507
5-th percentile59.8742576
Q171.392133
median89.191517
Q3111.3747885
95-th percentile157.9234415
Maximum232.513224
Range195.671717
Interquartile range (IQR)39.9826555

Descriptive statistics

Standard deviation29.84007262
Coefficient of variation (CV)0.3133478795
Kurtosis0.3936710173
Mean95.22985335
Median Absolute Deviation (MAD)19.5609885
Skewness0.9615700689
Sum95229853.35
Variance890.4299338
2020-07-31T10:10:49.248346image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
74.839213< 0.1%
 
70.7147813< 0.1%
 
96.0868383< 0.1%
 
76.4349913< 0.1%
 
74.6776083< 0.1%
 
90.6955193< 0.1%
 
72.1074173< 0.1%
 
88.3833433< 0.1%
 
67.6838823< 0.1%
 
115.7961593< 0.1%
 
Other values (993644)999970> 99.9%
 
ValueCountFrequency (%) 
36.8415071< 0.1%
 
37.9897071< 0.1%
 
39.1000471< 0.1%
 
39.4260821< 0.1%
 
39.7594051< 0.1%
 
ValueCountFrequency (%) 
232.5132241< 0.1%
 
231.7662371< 0.1%
 
226.6775131< 0.1%
 
224.5428441< 0.1%
 
223.7396731< 0.1%
 

peak-rpm
Real number (ℝ≥0)

Distinct count795296
Unique (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5059.449083098581
Minimum3576.659759
Maximum6975.934291
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:49.712841image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3576.659759
5-th percentile4246.949641
Q14800
median5107.621803
Q35355.311259
95-th percentile5847.253443
Maximum6975.934291
Range3399.274532
Interquartile range (IQR)555.3112593

Descriptive statistics

Standard deviation468.8905008
Coefficient of variation (CV)0.09267619718
Kurtosis-0.3372159325
Mean5059.449083
Median Absolute Deviation (MAD)307.621803
Skewness-0.04756327049
Sum5059449083
Variance219858.3018
2020-07-31T10:10:49.833268image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
480020443220.4%
 
5185.7237652< 0.1%
 
4431.6350382< 0.1%
 
5333.1755112< 0.1%
 
5853.1623982< 0.1%
 
5106.0288352< 0.1%
 
5091.0008172< 0.1%
 
5311.8043772< 0.1%
 
5278.7144612< 0.1%
 
5052.7808382< 0.1%
 
Other values (795286)79555079.6%
 
ValueCountFrequency (%) 
3576.6597591< 0.1%
 
3591.9856131< 0.1%
 
3627.4908181< 0.1%
 
3627.8758791< 0.1%
 
3628.073521< 0.1%
 
ValueCountFrequency (%) 
6975.9342911< 0.1%
 
6950.9627671< 0.1%
 
6779.0077371< 0.1%
 
6774.9023141< 0.1%
 
6772.6846431< 0.1%
 

city-mpg
Real number (ℝ≥0)

Distinct count971550
Unique (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.530282290655002
Minimum9.229116
Maximum58.519152
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:50.392887image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum9.229116
5-th percentile17.74742855
Q122.9404365
median26.1711805
Q329.58167275
95-th percentile37.7468223
Maximum58.519152
Range49.290036
Interquartile range (IQR)6.64123625

Descriptive statistics

Standard deviation5.877974145
Coefficient of variation (CV)0.2215571655
Kurtosis0.09416799495
Mean26.53028229
Median Absolute Deviation (MAD)3.311382
Skewness0.5314613908
Sum26530282.29
Variance34.55058005
2020-07-31T10:10:50.506742image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
25.9234194< 0.1%
 
26.5647784< 0.1%
 
26.3697624< 0.1%
 
26.5552854< 0.1%
 
29.5108164< 0.1%
 
27.3974334< 0.1%
 
26.3523794< 0.1%
 
23.0933154< 0.1%
 
23.9034834< 0.1%
 
25.9145994< 0.1%
 
Other values (971540)999960> 99.9%
 
ValueCountFrequency (%) 
9.2291161< 0.1%
 
12.383021< 0.1%
 
12.6042811< 0.1%
 
12.6253761< 0.1%
 
12.6265231< 0.1%
 
ValueCountFrequency (%) 
58.5191521< 0.1%
 
53.6445711< 0.1%
 
53.2365471< 0.1%
 
52.3069841< 0.1%
 
51.8196291< 0.1%
 

highway-mpg
Real number (ℝ≥0)

Distinct count972668
Unique (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.01049234188199
Minimum14.015803
Maximum61.303145
Zeros0
Zeros (%)0.0%
Memory size7.6 MiB
2020-07-31T10:10:51.064943image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum14.015803
5-th percentile22.3859976
Q128.28621525
median31.910125
Q335.48576275
95-th percentile43.8822787
Maximum61.303145
Range47.287342
Interquartile range (IQR)7.1995475

Descriptive statistics

Standard deviation6.156265082
Coefficient of variation (CV)0.1923202248
Kurtosis0.3881314078
Mean32.01049234
Median Absolute Deviation (MAD)3.6015115
Skewness0.4990222171
Sum32010492.34
Variance37.89959976
2020-07-31T10:10:51.176477image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
31.9172865< 0.1%
 
28.617015< 0.1%
 
29.1399594< 0.1%
 
32.1985214< 0.1%
 
32.6001144< 0.1%
 
32.5938744< 0.1%
 
29.4833364< 0.1%
 
31.9308994< 0.1%
 
37.1244024< 0.1%
 
29.3566314< 0.1%
 
Other values (972658)999958> 99.9%
 
ValueCountFrequency (%) 
14.0158031< 0.1%
 
15.5734061< 0.1%
 
15.7506871< 0.1%
 
15.7593291< 0.1%
 
15.8372891< 0.1%
 
ValueCountFrequency (%) 
61.3031451< 0.1%
 
60.7187551< 0.1%
 
60.6516861< 0.1%
 
60.5664071< 0.1%
 
60.4870421< 0.1%
 

Interactions

2020-07-31T10:09:09.984461image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:10.554491image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:10.972245image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:11.401686image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:11.818528image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:12.261282image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:12.679714image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:13.095803image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:13.525309image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:13.922852image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:14.339849image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:14.757884image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:15.204611image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:15.693559image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:16.128968image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:16.574542image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-31T10:09:17.492835image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:09:17.924101image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-31T10:09:18.786880image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-07-31T10:10:16.695671image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:17.110057image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:17.521274image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:17.928732image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:18.335901image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:18.729123image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:19.150336image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:19.563451image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:19.984395image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:20.387126image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:20.796276image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:21.191548image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:21.590621image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:22.000865image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:22.396477image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:22.800871image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:23.200333image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:23.590907image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:23.996006image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:24.378104image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:24.778404image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:25.184254image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:25.585930image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:25.987701image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:26.501978image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:26.892163image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:27.290745image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:27.700149image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:28.105557image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:28.510819image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:28.907521image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:29.300965image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:29.708624image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:30.086727image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:30.481904image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:30.886011image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:31.288058image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:31.682673image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-07-31T10:10:51.301948image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-07-31T10:10:51.541222image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-07-31T10:10:51.775398image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-07-31T10:10:52.016815image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-07-31T10:10:33.874568image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-07-31T10:10:34.886418image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

symbolingnormalized-losseswheel-baselengthwidthheightcurb-weightengine-sizeborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpg
00145.90617690.576742164.25791065.33901655.2574112119.914136108.0634423.6597663.4133797.431662126.7696404800.00000016.99845921.094419
1-192.733744105.905393187.25626868.85168653.5074413086.76322889.1387073.6931693.4369369.33576685.1991365954.20836716.83525423.957619
2087.62026996.992974172.98804465.35584755.2753912353.623357109.2615733.4811473.4097948.62590264.9631105207.82545027.96460432.979688
32107.85111296.083754166.74621465.49986951.5685243117.82153199.9795313.5985693.46171410.26277277.1549844076.99782926.99185631.718865
43149.36199499.346480178.44109166.34650551.0479622602.065082197.3281403.0697383.3243759.444737133.7457854800.00000014.24434622.570794
5298.33451096.406994183.50484964.73103157.9997692396.75868097.4583613.5765483.40837612.92974067.9338224800.00000039.48886628.457586
6196.61952294.684346150.83372263.18526652.0684192050.94181590.2262733.0455853.3451469.36233964.3796315954.97077628.01732828.402865
71117.26381699.025148152.61976568.66697356.0147581783.952625131.8058922.9069973.4177578.267729159.5375915288.49917624.07125633.062782
8299.15713196.708796180.17268463.93042052.7965162760.34491099.8533303.1678533.3037179.264914147.3085824800.00000017.04179825.763443
92131.84835093.529253159.26833563.47088652.5208442019.71363392.2112372.8748093.4042219.00000070.9833765279.59335029.33241436.756228

Last rows

symbolingnormalized-losseswheel-baselengthwidthheightcurb-weightengine-sizeborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpg
9999901115.40215990.570079163.83902364.44546354.5298051908.59684587.8947012.8890402.8289619.00000054.4959534800.00000040.96762241.850046
999991-1131.99586996.812853170.94069865.39708752.6334673409.854862109.1167603.1545693.3686578.628101114.0324944087.14361419.24478927.106228
999992-1126.874412103.744737172.07008464.22510155.8647442280.33063997.5914993.1270873.3081719.00000070.5959234800.00000029.08557635.831991
9999931114.34101995.291598170.93540664.24572950.6308982015.35549797.4595113.3399563.1746578.66077074.1099295459.68897034.43897643.471154
9999941164.52887789.452779158.82280363.92367054.6399222099.04144797.7971663.4975982.9141499.000000108.3673334800.00000022.67428230.037071
9999951131.88964394.683822152.60034863.75326055.0582481898.03599894.0515973.1357502.3334439.00000064.6372295215.31787839.61110532.011279
9999961111.52591694.187341164.68356264.37511750.0684051838.70040486.1079423.2607473.3880727.34474863.7104525356.35066841.57403636.153130
9999973142.307625111.712965189.70496168.34730752.3330882750.380050166.1129653.1669623.3283099.272152146.9664385207.10673029.56249132.558169
999998183.78438492.137350188.03488665.02924752.3751082322.327103101.0616423.1893053.0547128.76389568.8641985201.94351724.32107836.973930
9999992101.334427108.081357171.79737163.75883055.0651162122.46542689.6630242.8465503.3969198.02093367.2340995226.27062627.43438634.196120